Search

Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, although there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this article, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model rather than creating and maintaining an ensemble of models, e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and shows the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18]. [ABSTRACT FROM AUTHOR]

System emulation and firmware re-hosting have become popular techniques to answer various security and performance related questions, such as determining whether a firmware contain security vulnerabilities or meet timing requirements when run on a specific hardware platform. While this motivation for emulation and binary analysis has previously been explored and reported, starting to either work or research in the field is difficult. To this end, we provide a comprehensive guide for the practitioner or system emulation researcher. We layout common challenges faced during firmware re-hosting, explaining successive steps and surveying common tools used to overcome these challenges. We provide classification techniques on five different axes, including emulator methods, system type, fidelity, emulator purpose, and control. These classifications and comparison criteria enable the practitioner to determine the appropriate tool for emulation. We use our clas)sifications to categorize popular works in the field and present 28 common challenges faced when creating, emulating, and analyzing a system from obtaining firmwares to post emulation analysis. [ABSTRACT FROM AUTHOR]

Wireless reprogramming of sensor nodes is an essential requirement for long-lived networks since software functionality needs to be changed over time. During reprogramming, the number of radio transmissions should be minimized, since reprogramming time and energy depend chiefly on the number of radio transmissions. In this article, we present a multihop incremental reprogramming protocol called Zephyr that transfers the delta between old and new software versions, and lets the sensor nodes rebuild the new software using the received delta and the old software. Zephyr reduces the delta size by using application-level modifications to mitigate the effects of function shifts. Then it compares the two binary images at the byte level to generate a small delta, that is then sent over the wireless network to all the nodes. For the wide range of software change cases that we used as benchmarks, Zephyr transfers 1.83 to 1987 times less traffic through the network than Deluge (the standard nonincremental reprogramming protocol for TinyOS) and 1.14 to 49 times less traffic than an existing incremental reprogramming protocol by Jeong and Culler [2004]. [ABSTRACT FROM AUTHOR]

This article presents a hierarchical approach for detecting faults in wireless sensor networks (WSNs) after they have been deployed. The developers of WSNs can specify "invariants" that must be satisfied by the WSNs. We present a framework, Hierarchical SEnsor Network Debugging (H-SEND), for lightweight checking of invariants. H-SEND is able to detect a large class of faults in data-gathering WSNs, and leverages the existing message flow in the network by buffering and piggybacking messages. H-SEND checks as closely to the source of a fault as possible, pinpointing the fault quickly and efficiently in terms of additional network traffic. Therefore, H-SEND is suited to bandwidth or communication energy constrained networks. A specification expression is provided for specifying invariants so that a protocol developer can write behavioral level invariants. We hypothesize that data from sensor nodes does not change dramatically, but rather changes gradually over time. We extend our framework for the invariants that includes values determined at run-time in order to detect data trends. The value range can be based on information local to a single node or the surrounding nodes' values. Using our system, developers can write invariants to detect data trends without prior knowledge of correct values. Automatic value detection can be used to detect anomalies that cannot be detected in existing WSNs. To demonstrate the benefits of run-time range detection and fault checking, we construct a prototype WSN using CO2 and temperature sensors coupled to Mica2 motes. We show that our method can detect sudden changes of the environments with little overhead in communication, computation, and storage. [ABSTRACT FROM AUTHOR]